4.1 Gender prediction

When enough genes on X and Y chromosomes have detectable expression, these genes can be used to predict the gender of samples. In case the gender information is already available, the predicted gender can be used to identify potential mislabeling.

Figure 10. The average of Log2(Read Count+1) is calculated for X and Y chromosomes when there are at least 5 genes having detectable expression. The averages of each sample are plotted in this figure as X and Y axes. The averages of each chromosome are used separately to cluster samples using the pamk {fpc} function. If the optimal number of clusters is 2 for both chromosomes, gender prediction will be made, and the samples having predicted gender agreed byt the two chromosomes will be labeled with red (female) or blue (male). Click here to veiw the full table of gender prediction.

4.2 Hierarchical clustering

Hierarchical clustering groups samples based on their glabal correlation of all genes. It is a iterative procedure that merges two most similar samples/groups at each step.

Figure 11. Hierarchical clustering of samples using all autosomal genes. Normalized read counts are log2-transformed before clustering. The vertical position of the common node of two samples indicates their similarity (lower = more similar).

5.1 Reproduce this report

To reproduce this report:

1. Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory

2. To generate a new report using your own input data and parameter, edit the following items in the YAML file:

• output : where you want to put the output files
• home : the URL if you have a home page for your project
• analyst : your name
• description : background information about your project, analysis, etc.
• input : where are your input data, read instruction for preparing them
• parameter : parameters for this analysis; read instruction about how to prepare input data

3. Run the code below within R Console or RStudio, preferablly with a new R session:

if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }

CreateReport(filename.yaml);  # filename.yaml is the YAML file you just downloaded and edited for your analysis

If there is no complaint, go to the output folder and open the index.html file to view report.

5.2 Session information

## R version 3.3.3 (2017-03-06)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X Yosemite 10.10.5
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] edgeR_3.16.5               DESeq2_1.14.1             
##  [3] SummarizedExperiment_1.4.0 Biobase_2.34.0            
##  [5] limma_3.30.13              gplots_3.0.1              
##  [7] DEGandMore_0.0.0.9000      snow_0.4-2                
##  [9] awsomics_0.0.0.9000        Rnaseq_0.0.0.9000         
## [11] GenomicRanges_1.26.4       GenomeInfoDb_1.10.3       
## [13] IRanges_2.8.2              S4Vectors_0.12.2          
## [15] BiocGenerics_0.20.0        fpc_2.1-10                
## [17] vioplot_0.2                sm_2.2-5.4                
## [19] htmlwidgets_0.9            DT_0.2                    
## [21] yaml_2.1.16                knitr_1.18                
## [23] rmarkdown_1.10.3           RoCA_0.0.0.9000           
## [25] RCurl_1.95-4.9             bitops_1.0-6              
## [27] devtools_1.13.4            kableExtra_0.9.0          
## 
## loaded via a namespace (and not attached):
##  [1] bit64_0.9-7          RColorBrewer_1.1-2   httr_1.3.1          
##  [4] rprojroot_1.3-2      prabclus_2.2-6       tools_3.3.3         
##  [7] backports_1.1.2      R6_2.2.2             rpart_4.1-10        
## [10] KernSmooth_2.23-15   DBI_0.7              Hmisc_4.1-0         
## [13] lazyeval_0.2.1       colorspace_1.3-2     trimcluster_0.1-2   
## [16] nnet_7.3-12          withr_2.1.1          gridExtra_2.3       
## [19] bit_1.1-12           rvest_0.3.2          htmlTable_1.11.1    
## [22] xml2_1.1.1           checkmate_1.8.5      diptest_0.75-7      
## [25] caTools_1.17.1       scales_0.5.0         DEoptimR_1.0-8      
## [28] mvtnorm_1.0-6        robustbase_0.92-8    genefilter_1.56.0   
## [31] readr_1.1.1          stringr_1.2.0        digest_0.6.13       
## [34] foreign_0.8-67       XVector_0.14.1       base64enc_0.1-3     
## [37] pkgconfig_2.0.1      htmltools_0.3.6      highr_0.6           
## [40] rlang_0.1.6          RSQLite_2.0          rstudioapi_0.7      
## [43] jsonlite_1.5         mclust_5.4           BiocParallel_1.8.2  
## [46] gtools_3.5.0         acepack_1.4.1        magrittr_1.5        
## [49] modeltools_0.2-21    Formula_1.2-2        Matrix_1.2-8        
## [52] Rcpp_0.12.14         munsell_0.4.3        stringi_1.1.6       
## [55] MASS_7.3-45          zlibbioc_1.20.0      flexmix_2.3-14      
## [58] plyr_1.8.4           blob_1.1.0           grid_3.3.3          
## [61] gdata_2.18.0         lattice_0.20-34      splines_3.3.3       
## [64] annotate_1.52.1      hms_0.4.0            locfit_1.5-9.1      
## [67] pillar_1.1.0         geneplotter_1.52.0   XML_3.98-1.9        
## [70] evaluate_0.10.1      latticeExtra_0.6-28  data.table_1.10.4-3 
## [73] gtable_0.2.0         kernlab_0.9-25       ggplot2_2.2.1       
## [76] xtable_1.8-2         class_7.3-14         survival_2.40-1     
## [79] viridisLite_0.2.0    tibble_1.4.2         AnnotationDbi_1.36.2
## [82] memoise_1.1.0        cluster_2.0.5

END OF DOCUMENT